The effect of triglycerol diacrylate on the printability and properties of UV curable, bio-based nanohydroxyapatite composites
Bibliographic record
Abstract
3D printable biopolymer nanocomposites composed of hydroxyapatite nanoparticles and functionalized plant-based monomers demonstrate potential as sustainable and structural biomaterials. To increase this potential, their printability and performance must be improved. For extrusion-based 3D printing, such as Direct Ink Writing (DIW), printability is important for print fidelity. In this work, triglycerol diacrylate (TGDA) was added to an acrylated epoxidized soybean oil:polyethylene glycol diacrylate resin to increase hydrogen bonding. Greater hydrogen bonding was hypothesized to improve printability by increasing the ink's shear yield strength, and therefore shape holding after deposition. The effects of this additive on material and mechanical properties were quantified. Increased hydrogen bonding due to TGDA content increased the ink's shear yield stress and viscosity by 916% and 27.6%, respectively. This resulted in improved printability, with best performance at 3 vol% TGDA. This composition achieved an ultimate tensile strength (UTS) of 32.4 ± 2.1 MPa and elastic modulus of 1.15 ± 0.21 GPa. These were increased from the 0 vol% TGDA composite, which had an UTS of 24.8 ± 1.8 MPa and a modulus of 0.88 ± 0.06 GPa. This study demonstrates the development of bio-based additive manufacturing feedstocks for potential uses in sustainable manufacturing, rapid prototyping, and biomaterial applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".